scholarly journals Biodiversity–ecosystem functioning relationships in long-term time series and palaeoecological records: deep sea as a test bed

2016 ◽  
Vol 371 (1694) ◽  
pp. 20150282 ◽  
Author(s):  
Moriaki Yasuhara ◽  
Hideyuki Doi ◽  
Chih-Lin Wei ◽  
Roberto Danovaro ◽  
Sarah E. Myhre
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Time Scale ◽  
Deep Sea ◽  
Ecosystem Functioning ◽  
Time Series Data ◽  
Series Data ◽  
Ecosystem Stability ◽  
Test Bed ◽  
Biodiversity And Ecosystem Functioning

The link between biodiversity and ecosystem functioning (BEF) over long temporal scales is poorly understood. Here, we investigate biological monitoring and palaeoecological records on decadal, centennial and millennial time scales from a BEF framework by using deep sea, soft-sediment environments as a test bed. Results generally show positive BEF relationships, in agreement with BEF studies based on present-day spatial analyses and short-term manipulative experiments. However, the deep-sea BEF relationship is much noisier across longer time scales compared with modern observational studies. We also demonstrate with palaeoecological time-series data that a larger species pool does not enhance ecosystem stability through time, whereas higher abundance as an indicator of higher ecosystem functioning may enhance ecosystem stability. These results suggest that BEF relationships are potentially time scale-dependent. Environmental impacts on biodiversity and ecosystem functioning may be much stronger than biodiversity impacts on ecosystem functioning at long, decadal–millennial, time scales. Longer time scale perspectives, including palaeoecological and ecosystem monitoring data, are critical for predicting future BEF relationships on a rapidly changing planet.

scholarly journals Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

2021 ◽  
Author(s):  
Kei Ishida ◽  
Masato Kiyama ◽  
Ali Ercan ◽  
Motoki Amagasaki ◽  
Tongbi Tu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Time Series Data ◽  
Series Data ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Input Time ◽  
Target Data ◽  
Resolution Data

Abstract This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.

Wavelet-Based Feature Extraction for Behavior Recognition in Mobile Robots

2010 ◽  
Author(s):  
Xin Jin ◽  
Kushal Mukherjee ◽  
Shalabh Gupta ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Mobile Robots ◽  
Time Series Data ◽  
Piezoelectric Sensor ◽  
Relevant Information ◽  
Series Data ◽  
Test Bed ◽  
Behavior Recognition ◽  
Feature Extraction Method

This paper introduces a dynamic data-driven method for behavior recognition in mobile robots. The core concept of the paper is built upon the principle of symbolic dynamic filtering (SDF) that is used to extract relevant information in complex dynamical systems. The objective here is to identify the robot behavior from time-series data of piezoelectric sensor signals from the pressure sensitive floor in a laboratory environment. A symbolic feature extraction method is presented by partitioning of two-dimensional wavelet images of sensor time-series data. The K-nearest neighbors (k-NN) algorithm is used to identify the patterns extracted by SDF. The proposed method is validated by experimentation on a networked robotics test bed to detect and identify the type and motion profile of mobile robots.

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

scholarly journals Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

2018 ◽  
Vol 74 (9) ◽  
pp. 1461-1467 ◽  
Author(s):  
David A Raichlen ◽  
Yann C Klimentidis ◽  
Chiu-Hsieh Hsu ◽  
Gene E Alexander
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Time Series ◽  
Time Scales ◽  
Mortality Risk ◽  
Time Series Data ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Wide Range ◽  
Wearable Accelerometers

Abstract Background Accelerometers are included in a wide range of devices that monitor and track physical activity for health-related applications. However, the clinical utility of the information embedded in their rich time-series data has been greatly understudied and has yet to be fully realized. Here, we examine the potential for fractal complexity of actigraphy data to serve as a clinical biomarker for mortality risk. Methods We use detrended fluctuation analysis (DFA) to analyze actigraphy data from the National Health and Nutrition Examination Survey (NHANES; n = 11,694). The DFA method measures fractal complexity (signal self-affinity across time-scales) as correlations between the amplitude of signal fluctuations in time-series data across a range of time-scales. The slope, α, relating the fluctuation amplitudes to the time-scales over which they were measured describes the complexity of the signal. Results Fractal complexity of physical activity (α) decreased significantly with age (p = 1.29E−6) and was lower in women compared with men (p = 1.79E−4). Higher levels of moderate-to-vigorous physical activity in older adults and in women were associated with greater fractal complexity. In adults aged 50–79 years, lower fractal complexity of activity (α) was associated with greater mortality (hazard ratio = 0.64; 95% confidence interval = 0.49–0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality. Conclusions Wearable accelerometers can provide a noninvasive biomarker of physiological aging and mortality risk after adjusting for other factors strongly associated with mortality. Thus, this fractal analysis of accelerometer signals provides a novel clinical application for wearable accelerometers, advancing efforts for remote monitoring of physiological health by clinicians.

Approach to Forecasting Occurrence of Slope Failure with Nonlinear Dynamical System

2013 ◽  
Vol 438-439 ◽  
pp. 1597-1602
Author(s):  
Han Dong Liu
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Time Scale ◽  
Slope Failure ◽  
Time Series Data ◽  
Nonlinear Dynamical System ◽  
Series Data ◽  
Geologic Hazard ◽  
Nonlinear Dynamical ◽  
Short Term Prediction

Landslides constitute a major geologic hazard because they are widespread and commonly occur in connection with other major natural disasters such as earthquakes, rainstorms, wildfires and floods. Nonlinear dynamical system (NDS) techniques have been developed to analyze chaotic time series data. According to NDS theory, the correlation dimension and predictable time scale are evaluated from a single observed time series. The Xintan landslide case study is presented to demonstrate that chaos exists in the evolution of a landslide and the predictable time scale must be considered. The possibility for long-term, medium-term and short-term prediction of landslide is discussed.

scholarly journals STFT-LDA: An algorithm to facilitate the visual analysis of building seismic responses

2021 ◽  
pp. 147387162110386
Author(s):  
Zhenge Zhao ◽  
Danilo Motta ◽  
Matthew Berger ◽  
Joshua A Levine ◽  
Ismail B Kuzucu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Visual Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time Scales ◽  
Prototype System ◽  
Multiple Time ◽  
Seismic Responses ◽  
Large Ensembles

Civil engineers use numerical simulations of a building’s responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to determine the latter to prevent the former. Such simulations generate large ensembles of multivariate, multiattribute time series. Comprehensive understanding of this data requires techniques that support the multivariate nature of the time series and can compare behaviors that are both periodic and non-periodic across multiple time scales and multiple time series themselves. In this paper, we present a novel technique to extract such patterns from time series generated from simulations of seismic responses. The core of our approach is the use of topic modeling, where topics correspond to interpretable and discriminative features of the earthquakes. We transform the raw time series data into a time series of topics, and use this visual summary to compare temporal patterns in earthquakes, query earthquakes via the topics across arbitrary time scales, and enable details on demand by linking the topic visualization with the original earthquake data. We show, through a surrogate task and an expert study, that this technique allows analysts to more easily identify recurring patterns in such time series. By integrating this technique in a prototype system, we show how it enables novel forms of visual interaction.

MULTIFRACTAL ANALYSIS OF TEMPERATURE TIME SERIES: DATA FROM BOREHOLES IN KAMCHATKA

Fractals ◽  
2005 ◽  
Vol 13 (04) ◽  
pp. 299-310 ◽  
Author(s):  
LOUISE BODRI ◽  
VLADIMIR CERMAK
Keyword(s):  
Heat Transfer ◽  
Time Series ◽  
Time Scales ◽  
Time Series Data ◽  
Heat Transfer Process ◽  
Series Data ◽  
Local Growth ◽  
Forming Process ◽  
Low Frequencies ◽  
Second Moment

In the present work, we focus on the multifractal structure of the microtemperature time series monitored at depth in boreholes, namely in two holes drilled in Kamchatka (Russia). Two monitoring series were performed for approximately two weeks with a 5-second reading interval; thus, each series contains about 230,000 data points. The observed temperatures displayed sharp gradients and large fluctuations over all observed time ranges, reflecting fine structure of the heat transfer process in the shallow subsurface. Recurrence plot (RP) technique was applied for detecting hidden rhythms that generated the time series. The most characteristic feature of the RPs is their web-like structure, indicating quasi-periodic occurrence of the sharp changes. The spectral and the local growth of the second moment techniques were used for distinguishing between the potentially different heat transfer processes. Both spectra show "red noise" behavior, however, exhibit distinct scaling exponents for different frequency domains from near 1.33 for low frequencies to near 3 at the high frequency end of the spectra. Local growth of the second moment technique has revealed the presence of temperature forming process with the characteristic time of approximately 1 minute, that smoothes out generally anti-persistent behavior on shorter time scales, but has a little effect on longer time scales. The investigation was accomplished by the calculation of universal multifractal indices, which characterize temperature fluctuation upon scales in the range from minutes to weeks. Both time series show very similar multifractal behavior. The Hurst exponent, characterizing the degree of non-stationarity, equals to 0.18–0.20, the measure of intermittence C1 amounts to 0.10, and the α-index, characterizing the degree of multifractality, amounts to 1.32 and 1.24 for both boreholes. We speculate on the origin of these common features intervening all over the observed range of time scales in a borehole.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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